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Home > Data > Deep Learning > The Deep Learning Workshop
The Deep Learning Workshop
The Deep Learning Workshop

The Deep Learning Workshop: Learn the skills you need to develop your own next-generation deep learning models with TensorFlow and Keras

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The Deep Learning Workshop

2. Neural Networks

Overview

This chapter starts with an introduction to biological neurons; we see how an artificial neural network is inspired by biological neural networks. We will examine the structure and inner workings of a simple single-layer neuron called a perceptron and learn how to implement it in TensorFlow. We will move on to building multilayer neural networks to solve more complex multiclass classification tasks and discuss the practical considerations of designing a neural network. As we build deep neural networks, we will move on to Keras to build modular and easy-to-customize neural network models in Python. By the end of this chapter, you'll be adept at building neural networks to solve complex problems.

Introduction

In the previous chapter, we learned how to implement basic mathematical concepts such as quadratic equations, linear algebra, and matrix multiplication in TensorFlow. Now that we have learned the basics, let's dive into Artificial Neural Networks (ANNs), which are central to artificial intelligence and deep learning.

Deep learning is a subset of machine learning. In supervised learning, we often use traditional machine learning techniques, such as support vector machines or tree-based models, where features are explicitly engineered by humans. However, in deep learning, the model explores and identifies the important features of a labeled dataset without human intervention. ANNs, inspired by biological neurons, have a layered representation, which helps them learn labels incrementally—from the minute details to the complex ones. Consider the example of image recognition: in a given image, an ANN would just as easily identify basic details such as light and...

Neural Networks and the Structure of Perceptrons

A neuron is a basic building block of the human nervous system, which relays electric signals across the body. The human brain consists of billions of interconnected biological neurons, and they are constantly communicating with each other by sending minute electrical binary signals by turning themselves on or off. The general meaning of a neural network is a network of interconnected neurons. In the current context, we are referring to ANNs, which are actually modeled on a biological neural network. The term artificial intelligence is derived from the fact that natural intelligence exists in the human brain (or any brain for that matter), and we humans are trying to simulate this natural intelligence artificially. Though ANNs are inspired by biological neurons, some of the advanced neural network architectures, such as CNNs and RNNs, do not actually mimic the behavior of a biological neuron. However, for ease of understanding, we will...

Training a Perceptron

To train a perceptron, we need the following components:

  • Data representation
  • Layers
  • Neural network representation
  • Loss function
  • Optimizer
  • Training loop

In the previous section, we covered most of the preceding components: the data representation of the input data and the true labels in TensorFlow. For layers, we have the linear layer and the activation functions, which we saw in the form of the net input function and the sigmoid function respectively. For the neural network representation, we made a function called perceptron(), which uses a linear layer and a sigmoid layer to perform predictions. What we did in the previous section using input data and initial weights and biases is called forward propagation. The actual neural network training involves two stages: forward propagation and backward propagation. We will explore them in detail in the next few steps. Let's look at the training process at a higher level:

    ...

Keras as a High-Level API

In TensorFlow 1.0, there were several APIs, such as Estimator, Contrib, and layers. In TensorFlow 2.0, Keras is very tightly integrated with TensorFlow, and it provides a high-level API that is user-friendly, modular, composable, and easy to extend in order to build and train deep learning models. This also makes developing code for neural networks much easier. Let's see how it works.

Exercise 2.05: Binary Classification Using Keras

In this exercise, we will implement a very simple binary classifier with a single neuron using the Keras API. We will use the same data.csv file that we used in Exercise 2.02, Perceptron as a Binary Classifier:

Note

The dataset can be downloaded from GitHub by accessing the following GitHub link: https://packt.live/2BVtxIf.

  1. Import the required libraries:
    import tensorflow as tf
import pandas as pd
import matplotlib.pyplot as plt
%matplotlib inline
# Import Keras libraries
from tensorflow.keras.models import...

Exploring the Optimizers and Hyperparameters of Neural Networks

Training a neural network to get good predictions requires tweaking a lot of hyperparameters such as optimizers, activation functions, the number of hidden layers, the number of neurons in each layer, the number of epochs, and the learning rate. Let's go through each of them one by one and discuss them in detail.

Gradient Descent Optimizers

In an earlier section titled Perceptron Training Process in TensorFlow, we briefly touched upon the gradient descent optimizer without going into the details of how it works. This is a good time to explore the gradient descent optimizer in a little more detail. We will provide an intuitive explanation without going into the mathematical details.

The gradient descent optimizer's function is to minimize the loss or error. To understand how gradient descent works, you can think of this analogy: imagine a person at the top of a hill who wants to reach the bottom...

Activity 2.01: Build a Multilayer Neural Network to Classify Sonar Signals

In this activity, we will use the Sonar dataset (https://archive.ics.uci.edu/ml/datasets/Connectionist+Bench+(Sonar,+Mines+vs.+Rocks)), which has patterns obtained by bouncing sonar signals off a metal cylinder at various angles and under various conditions. You will build a neural network-based classifier to classify between sonar signals bounced off a metal cylinder (the Mine class), and those bounced off a roughly cylindrical rock (the Rock class). We recommend using the Keras API to make your code more readable and modular, which will allow you to experiment with different parameters easily:

Note

You can download the sonar dataset from this link https://packt.live/31Xtm9M.

  1. The first step is to understand the data so that you can figure out whether this is a binary classification problem or a multiclass classification problem.
  2. Once you understand the data and the type of classification that...

Summary

In this chapter, we started off by looking at biological neurons and then moved on to artificial neurons. We saw how neural networks work and took a practical approach to building single-layer and multilayer neural networks to solve supervised learning tasks. We looked at how a perceptron works, which is a single unit of a neural network, all the way to a deep neural network capable of performing multiclass classification. We saw how Keras makes it very easy to create deep neural networks with a minimal amount of code. Lastly, we looked at practical considerations to take into account when building a successful neural network, which involved important concepts such as gradient descent optimizers, overfitting, and dropout.

In the next chapter, we will go to the next level and build a more complicated neural network called a CNN, which is widely used in image recognition.

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Key benefits

  • Understand how to implement deep learning with TensorFlow and Keras
  • Learn the fundamentals of computer vision and image recognition
  • Study the architecture of different neural networks

Description

Are you fascinated by how deep learning powers intelligent applications such as self-driving cars, virtual assistants, facial recognition devices, and chatbots to process data and solve complex problems? Whether you are familiar with machine learning or are new to this domain, The Deep Learning Workshop will make it easy for you to understand deep learning with the help of interesting examples and exercises throughout. The book starts by highlighting the relationship between deep learning, machine learning, and artificial intelligence and helps you get comfortable with the TensorFlow 2.0 programming structure using hands-on exercises. You’ll understand neural networks, the structure of a perceptron, and how to use TensorFlow to create and train models. The book will then let you explore the fundamentals of computer vision by performing image recognition exercises with convolutional neural networks (CNNs) using Keras. As you advance, you’ll be able to make your model more powerful by implementing text embedding and sequencing the data using popular deep learning solutions. Finally, you’ll get to grips with bidirectional recurrent neural networks (RNNs) and build generative adversarial networks (GANs) for image synthesis. By the end of this deep learning book, you’ll have learned the skills essential for building deep learning models with TensorFlow and Keras.

Who is this book for?

If you are interested in machine learning and want to create and train deep learning models using TensorFlow and Keras, this workshop is for you. A solid understanding of Python and its packages, along with basic machine learning concepts, will help you to learn the topics quickly.

What you will learn

  • Understand how deep learning, machine learning, and artificial intelligence are different
  • Develop multilayer deep neural networks with TensorFlow
  • Implement deep neural networks for multiclass classification using Keras
  • Train CNN models for image recognition
  • Handle sequence data and use it in conjunction with RNNs
  • Build a GAN to generate high-quality synthesized images
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Publication date : Jul 31, 2020
Length: 474 pages
Edition : 1st
Language : English
ISBN-13 : 9781839219856
Category :
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Product Details

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Length: 474 pages
Edition : 1st
Language : English
ISBN-13 : 9781839219856
Category :
Data
Languages :
Python
Concepts :
Deep Learning
Tools :
Keras

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Table of Contents

7 Chapters
1. Building Blocks of Deep Learning Chevron down icon Chevron up icon
1. Building Blocks of Deep Learning
Introduction
Introduction to TensorFlow
Summary
2. Neural Networks Chevron down icon Chevron up icon
2. Neural Networks
Introduction
Neural Networks and the Structure of Perceptrons
Training a Perceptron
Keras as a High-Level API
Exploring the Optimizers and Hyperparameters of Neural Networks
Activity 2.01: Build a Multilayer Neural Network to Classify Sonar Signals
Summary
3. Image Classification with Convolutional Neural Networks (CNNs) Chevron down icon Chevron up icon
3. Image Classification with Convolutional Neural Networks (CNNs)
Introduction
Digital Images
Image Processing
Convolutional Neural Networks
Pooling Layers
Data Augmentation
Saving and Restoring Models
Transfer Learning
Fine-Tuning
Summary
4. Deep Learning for Text – Embeddings Chevron down icon Chevron up icon
4. Deep Learning for Text – Embeddings
Introduction
Deep Learning for Natural Language Processing
Classical Approaches to Text Representation
Distributed Representation for Text
Summary
5. Deep Learning for Sequences Chevron down icon Chevron up icon
5. Deep Learning for Sequences
Introduction
Working with Sequences
Recurrent Neural Networks
Summary
6. LSTMs, GRUs, and Advanced RNNs Chevron down icon Chevron up icon
6. LSTMs, GRUs, and Advanced RNNs
Introduction
Long-Range Dependence/Influence
The Vanishing Gradient Problem
Sequence Models for Text Classification
The Embedding Layer
Building the Plain RNN Model
Making Predictions on Unseen Data
LSTMs, GRUs, and Other Variants
Parameters in an LSTM
LSTM versus Plain RNNs
Gated Recurrence Units
Bidirectional RNNs
Stacked RNNs
Summarizing All the Models
Attention Models
More Variants of RNNs
Summary
7. Generative Adversarial Networks Chevron down icon Chevron up icon
7. Generative Adversarial Networks
Introduction
Deep Convolutional GANs
Summary

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Customer reviews

Rating distribution
Full star icon Full star icon Full star icon Full star icon Half star icon 4.5
(4 Ratings)
5 star 50%
4 star 50%
3 star 0%
2 star 0%
1 star 0%
James Le Nov 03, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
The book makes it easy for you to understand deep learning with the help of interesting examples and exercises throughout. There are 7 chapters in total:- Chapter 1 discusses the practical applications of deep learning.- Chapter 2 teaches you the structure of artificial neural networks.- Chapter 3 covers image processing, how it works, and how that knowledge can be applied to Convolutional Neural Networks (CNNs).- Chapter 4 introduces you to the world of Natural Language Processing.- Chapter 5 shows you how to work on a classic sequence processing task—stock price prediction.- Chapter 6 reviews RNNs' practical drawbacks and how Long Short Term Memory (LSTM) models help overcome them.- Chapter 7 introduces you to generative adversarial networks (GANs) and their basic components.All the chapters provide hands-on exercises for you to work on (using TensorFlow 2.0 and Keras). With more than 400 pages of content, this is a comprehensive coverage of deep learning fundamentals from a programming-intensive perspective!
Amazon Verified review Amazon
Luis René Mata Quiñonez Nov 09, 2020
Full star icon Full star icon Full star icon Full star icon Full star icon 5
This is a very good book on deep learning. It is very focused on the most important DL applications by using Keras and TensorFlow. It provides a hands-on approach where examples and models can be easily translated to solve different problems.
Amazon Verified review Amazon
Jackie K. Dec 13, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This is a decent intro to deep learning that covers Tensorflow and Keras. I was torn between 3 stars and 4. The content from what I can see is good from a nuts and bolts perspective, but there are a few negative aspects to the book. I will just cover one, because I think it is the most important. Many mistakes are made in science by a lack of representation. For example chatbots, don't recognize women's voices well, and there have also been issues with the recognition of various skin colors because models have been trained on bias datasets. None of these issues were brought up in the book even in passing with information on where to learn more. From a nuts and bolts perspective this book is a 4. From a comprehensive intro perspective this book is 3.
Amazon Verified review Amazon
Hakuna Matata Oct 17, 2020
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
This is a good introductory book on deep learning - gives you a good tour of all the core fundamentals. Also, I loved the fact that you could try out the examples and code snippets using binger.org infrastructure - this way you don't have to mess with setting up your own environment to run and test the code snippets.One nit is no mention of any of the latest transformer architecture or modern language models such as BERT and RoBERTa.
Amazon Verified review Amazon

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About the 5 authors

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Profile icon Mirza Rahim Baig
Mirza Rahim Baig
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Mirza Rahim Baig is a Data Science and Artificial Intelligence leader with over 13 years of experience across e-commerce, healthcare, and marketing. He currently holds the position of leading Product Analytics at Marketing Services for Zalando, Europe's largest online fashion platform. In addition, he serves as a Subject Matter Expert and faculty member for MS level programs at prominent Ed-Tech platforms and institutes in India. He is also the lead author of two books, 'Data Science for Marketing Analytics' and 'The Deep Learning Workshop,' both published by Packt. He is recognized as a thought leader in my field and frequently participates as a guest speaker at various forums.
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Profile icon Thomas Joseph
Thomas Joseph
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Thomas V. Joseph is a data science practitioner, researcher, trainer, mentor, and writer with more than 19 years of experience. He has extensive experience in solving business problems using machine learning toolsets across multiple industry segments.
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Profile icon Anthony So
Anthony So
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Anthony So is a renowned leader in data science. He has extensive experience in solving complex business problems using advanced analytics and AI in different industries including financial services, media, and telecommunications. He is currently the chief data officer of one of the most innovative fintech start-ups. He is also the author of several best-selling books on data science, machine learning, and deep learning. He has won multiple prizes at several hackathon competitions, such as Unearthed, GovHack, and Pepper Money. Anthony holds two master's degrees, one in computer science and the other in data science and innovation.
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Profile icon Nipun Sadvilkar
Nipun Sadvilkar
Nipun Sadvilkar is a senior data scientist at US healthcare company leading a team of data scientists and subject matter expertise to design and build the clinical NLP engine to revamp medical coding workflows, enhance coder efficiency, and accelerate revenue cycle. He has experience of more than 3 years in building NLP solutions and web-based data science platforms in the area of healthcare, finance, media, and psychology. His interests lie at the intersection of machine learning and software engineering with a fair understanding of the business domain. He is a member of the regional and national python community. He is author of pySBD - an NLP open-source python library for sentence segmentation which is recognized by ExplosionAI (spaCy) and AllenAI (scispaCy) organizations.
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Profile icon Mohan Kumar Silaparasetty
Mohan Kumar Silaparasetty
Mohan Kumar Silaparasetty is seasoned deep learning and AI professional. He is a graduate from IIT Kharagpur with more than 25 years of industry experience in a variety of roles. After having a successful corporate career, Mohan embarked on his entrepreneurial journey and is the co-founder and CEO of Trendwise Analytics. This company provides consulting and training in AI and deep learning. He is also the organizer of the Bangalore Artificial intelligence Meetup group with over 3500 members.
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Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela
What is custom duty/charge? Chevron down icon Chevron up icon

Customs duty are charges levied on goods when they cross international borders. It is a tax that is imposed on imported goods. These duties are charged by special authorities and bodies created by local governments and are meant to protect local industries, economies, and businesses.

Do I have to pay customs charges for the print book order? Chevron down icon Chevron up icon

The orders shipped to the countries that are listed under EU27 will not bear custom charges. They are paid by Packt as part of the order.

List of EU27 countries: www.gov.uk/eu-eea:

A custom duty or localized taxes may be applicable on the shipment and would be charged by the recipient country outside of the EU27 which should be paid by the customer and these duties are not included in the shipping charges been charged on the order.

How do I know my custom duty charges? Chevron down icon Chevron up icon

The amount of duty payable varies greatly depending on the imported goods, the country of origin and several other factors like the total invoice amount or dimensions like weight, and other such criteria applicable in your country.

For example:

  • If you live in Mexico, and the declared value of your ordered items is over $ 50, for you to receive a package, you will have to pay additional import tax of 19% which will be $ 9.50 to the courier service.
  • Whereas if you live in Turkey, and the declared value of your ordered items is over € 22, for you to receive a package, you will have to pay additional import tax of 18% which will be € 3.96 to the courier service.
How can I cancel my order? Chevron down icon Chevron up icon

Cancellation Policy for Published Printed Books:

You can cancel any order within 1 hour of placing the order. Simply contact customercare@packt.com with your order details or payment transaction id. If your order has already started the shipment process, we will do our best to stop it. However, if it is already on the way to you then when you receive it, you can contact us at customercare@packt.com using the returns and refund process.

Please understand that Packt Publishing cannot provide refunds or cancel any order except for the cases described in our Return Policy (i.e. Packt Publishing agrees to replace your printed book because it arrives damaged or material defect in book), Packt Publishing will not accept returns.

What is your returns and refunds policy? Chevron down icon Chevron up icon

Return Policy:

We want you to be happy with your purchase from Packtpub.com. We will not hassle you with returning print books to us. If the print book you receive from us is incorrect, damaged, doesn't work or is unacceptably late, please contact Customer Relations Team on customercare@packt.com with the order number and issue details as explained below:

  1. If you ordered (eBook, Video or Print Book) incorrectly or accidentally, please contact Customer Relations Team on customercare@packt.com within one hour of placing the order and we will replace/refund you the item cost.
  2. Sadly, if your eBook or Video file is faulty or a fault occurs during the eBook or Video being made available to you, i.e. during download then you should contact Customer Relations Team within 14 days of purchase on customercare@packt.com who will be able to resolve this issue for you.
  3. You will have a choice of replacement or refund of the problem items.(damaged, defective or incorrect)
  4. Once Customer Care Team confirms that you will be refunded, you should receive the refund within 10 to 12 working days.
  5. If you are only requesting a refund of one book from a multiple order, then we will refund you the appropriate single item.
  6. Where the items were shipped under a free shipping offer, there will be no shipping costs to refund.

On the off chance your printed book arrives damaged, with book material defect, contact our Customer Relation Team on customercare@packt.com within 14 days of receipt of the book with appropriate evidence of damage and we will work with you to secure a replacement copy, if necessary. Please note that each printed book you order from us is individually made by Packt's professional book-printing partner which is on a print-on-demand basis.

What tax is charged? Chevron down icon Chevron up icon

Currently, no tax is charged on the purchase of any print book (subject to change based on the laws and regulations). A localized VAT fee is charged only to our European and UK customers on eBooks, Video and subscriptions that they buy. GST is charged to Indian customers for eBooks and video purchases.

What payment methods can I use? Chevron down icon Chevron up icon

You can pay with the following card types:

  1. Visa Debit
  2. Visa Credit
  3. MasterCard
  4. PayPal
What is the delivery time and cost of print books? Chevron down icon Chevron up icon

Shipping Details

USA:

'

Economy: Delivery to most addresses in the US within 10-15 business days

Premium: Trackable Delivery to most addresses in the US within 3-8 business days

UK:

Economy: Delivery to most addresses in the U.K. within 7-9 business days.
Shipments are not trackable

Premium: Trackable delivery to most addresses in the U.K. within 3-4 business days!
Add one extra business day for deliveries to Northern Ireland and Scottish Highlands and islands

EU:

Premium: Trackable delivery to most EU destinations within 4-9 business days.

Australia:

Economy: Can deliver to P. O. Boxes and private residences.
Trackable service with delivery to addresses in Australia only.
Delivery time ranges from 7-9 business days for VIC and 8-10 business days for Interstate metro
Delivery time is up to 15 business days for remote areas of WA, NT & QLD.

Premium: Delivery to addresses in Australia only
Trackable delivery to most P. O. Boxes and private residences in Australia within 4-5 days based on the distance to a destination following dispatch.

India:

Premium: Delivery to most Indian addresses within 5-6 business days

Rest of the World:

Premium: Countries in the American continent: Trackable delivery to most countries within 4-7 business days

Asia:

Premium: Delivery to most Asian addresses within 5-9 business days

Disclaimer:
All orders received before 5 PM U.K time would start printing from the next business day. So the estimated delivery times start from the next day as well. Orders received after 5 PM U.K time (in our internal systems) on a business day or anytime on the weekend will begin printing the second to next business day. For example, an order placed at 11 AM today will begin printing tomorrow, whereas an order placed at 9 PM tonight will begin printing the day after tomorrow.


Unfortunately, due to several restrictions, we are unable to ship to the following countries:

  1. Afghanistan
  2. American Samoa
  3. Belarus
  4. Brunei Darussalam
  5. Central African Republic
  6. The Democratic Republic of Congo
  7. Eritrea
  8. Guinea-bissau
  9. Iran
  10. Lebanon
  11. Libiya Arab Jamahriya
  12. Somalia
  13. Sudan
  14. Russian Federation
  15. Syrian Arab Republic
  16. Ukraine
  17. Venezuela